The final GAFIS report is out. “Big banks can’t serve low-income clients because small balance accounts just do not offer a sustainable business proposition” has been a truism since forever, and for good reason. This report takes a good stab at how that doesn’t always have to be the case, and how some of the biggest banks in sizeable developing countries are finding creative, segmented solutions to expanding the envelope of financial inclusion.

Between 2010 and 2013, the GAFIS project engaged five banks – Bancolombia (Colombia), BANSEFI (Mexico), Equity Bank (Kenya), ICICI Bank (India), and Standard Bank (South Africa). Together, they serve 77 million clients, and have combined assets of $250 billion, many of whom are the low-income segment. Obviously not all low-balance accounts belong to low-income clients, but the vast preponderance of low-income clients will have low-balance accounts; hence the focus on making them sustainable.

The final impact was described as follows (p.g. 38):

Collectively, the GAFIS banks have opened more than 4.2 million new accounts in GAFIS-linked products in less than three years. More importantly, they now serve approximately 420,000 “new, poor savers” as a result of the project (see Box I). This number is measured according to the GAFIS project definition, which requires evidence of both savings activity (870,238 accounts) and the poverty status of the account holders (543,119 new accounts meet both criteria); and which also weights the level of attribution to the project according to how directly GAFIS was involved (reducing the 543,119 to 419,654). Even with delays in launching new products at several banks, these growing numbers provide early indication that large banks can achieve scale outreach with their new propositions.

Very importantly, the business case for these 420k new, poor saver accounts was improved significantly through a combination of strategies:

Costing methodology adjusted to product and channel specifications: It doesn’t make sense to load up branch costs into an acquisition expense if the product is mostly mobile phone based, for example.

Using agent channels, where agents are essentially mom-and-pop stores set up to function as mini-banks: Transactions cost about a fifth as much with agents, where the agent is paid a commission. Account origination costs are in the $1-$6 range at agents, while those at branches are $16-$25.

Increasing cross-sell: This might mean serving credit needs of clients, or fees from intermediating inflows from government or other sources of credit payments.

Here’s a summary of what each bank did to improve the business case (p.g. 22):

Still, all this does not make the business case for the accounts a positive one. It does significantly improve it though, from losing $2.79 per account per month, to losing $1.02 per account per month (p.g. 32):

Btw, spoiler alert: she’s part of the “microcredit is bad for poor people, mmkay?” crowd. Don’t let that detract from her message though – this stuff is worth keeping in mind so that we don’t have to deal with another AP-style disaster again.

A friend of mine recently forwarded me a recent publication titled The Limitations of Microcredit for Promoting Microenterprises in Bangladesh that appeared in the Jan-Mar 2012 edition of the Economic Annals. I sort of feel bad for picking on this one study but it’s somewhat representative of a few others I’ve seen where a couple of things just bothered me a brick ton.. Here’s a sampling. Apologies in advance to the authors; and I’m sure my turn will come soon enough

Claim 1:The field survey shows that about 11.7% of the microcredit borrowers are this kind of potential or growing microentrepreneur (Abstract).

Except, the survey this paper is based on was not randomly sampled (p.g. 43):

Samples were selected from urban (32.4%), semi-urban (27.2%), and rural (40.4%) areas, to ensure that microborrowers of different sized loans engaged in various categories of economic operations in rural and urban settings were adequately represented. In the absence of full knowledge of the structure and distribution of the microcredit borrower population in the country, random sampling as representative sampling is neither possible nor desirable (Molla and Alam, 2011). Moreover, in many situations random sampling is neither effective nor cost effective in serving the purpose for which sample data are collected. Purposive or judgment sampling is effectively used in such cases. Accordingly, a judgment sampling procedure was thought more effective and appropriate for this survey.

A couple of things:

The 11.7% is not representative of the universe of Bangladeshi microcredit recipients, but of the non-random sample used in the study. Indeed, unless the distribution of microcredit borrowers is exactly 32.4%:27.2%:40.4% over urban:semi-urban:rural areas, the number is anything but 11.7%. Not a lot of MFIs want to work in urban areas, specially slums, where a large proportion of the urban poor and ultra-poor live. Ask Shakti Foundation, one of the few MFIs that serve this challenging demographic. The real number could be 5%, it could be 20% – who knows..

I don’t buy the excuse that random sampling is not appropriate. By the authors’ own admissions, there are 15 million microcredit borrowers. (I think that’s a low ball number, but let’s go with it for now.) There are 150 million people in the country, including infants, the middle class, the elderly – demographics which are not obvious target populations of MFIs. You are almost guaranteed to hit a MFI borrower if you throw a … pillow a few times. Purposive or judgemental sampling is done when you are either targeting a very specific group and you don’t care about being representative, or there are so few of those you want to talk to that you have to search them out with deliberation. I can’t figure out how that could possibly be the case here.

I find the suggestion that “in the absence of full knowledge of the structure and distribution of the microcredit borrower population in the country, random sampling as representative sampling is neither possible nor desirable” quite counter-intuitive. Indeed, if one does not know the underlying distribution of borrowers, a random sample would have illuminated that unknown too, contributing to the findings of this paper. Also, one has to look at the big three – Grameen, BRAC and ASA- and one can guesstimate fairly accurately what the distribution is..

It would have made much more sense to present the findings in three silos – urban, semi-urban and rural, and share all the findings within those segments. It would have been more appropriate than as an aggregate too since, conceivably, the urban implications of microcredit on microenterprises is somewhat different from rural ones.

Claim 2:A sizeable chunk of all borrowers, microentrepreneurs or not, have issues with the terms of credit, which are inadequate for entrepreneurial purposes. (p.g. 47, my summary)

The entire para is as follows:

About 20.7% of all the borrowers and 15.4% of the microenterprise borrowers believe that they do not have the scope to effectively use the entire loan amount at the start of activities. In practice about 29.2% of all the borrowers and 20% of the microenterprise borrowers did not use the entire loan amount at the start of their business operations (Table 3). On the other hand, about 27.9% of all the borrowers and 55.4% of the microenterprise operators had to top-up the loan fund with personal or other borrowed funds to start operations. On top of that about 21.4% of all the borrowers and 8.6% of the microenterprise operators invested additional funds during the year, either from personal sources or from credits obtained from other microcredit providers. About 28.3% of all the sample clients and 40% of the microenterprise clients received multiple loans (2-3 or more) from 2-3 or more microcredit institutions.

I agree with the general thrust of the message. The rigid disbursement and repayment schedules are not conducive to the fluid needs of business, and borrowers often have to borrow from other sources to make up working capital shortfalls.

But the numbers I see here actually don’t seem that bad:

If 20% of borrowers don’t believe they can use the entire loan amount right away, then 80% believe they can, right?

Similarly, if almost 3/4 of borrowers and 1/2 of microentrepreneurs do not have to top-up funds right at the beginning, that’s not too bad, right?

Also, similarly, if almost 80% of borrowers and > 90% of microentrepreneurs did not have to invest additional funds, that’s not terrible either, right?

40% of the clients borrowing from multiple MFIs could be seen as a bad thing, but we have to be careful not to equate miltiple-borrowing with overindebtedness. PotP is chock full of examples which clearly demonstrate how sophisticated the poor are in their financial management, and Bangladesh was one of the study countries too.

I mean, it looks like microcredit is able to satisfy funding needs at various levels for 75-80% of borrowers in general and 50-90% of microentrepreneurs in particular, more or less. If we demand more, are we not holding microcredit to an unrealistically high standard, given the realities of the products and distribution channels?

Again, the bone I pick is not with the underlying message, but that the numbers put forward seem to weaken the case being made.

Claim 3:(The) preference for women as clients for credit is found to be a strong methodological limitation of the microcredit delivery system in promoting microenterprises. (p.g. 45-46)

The authors make a compelling enough case, up to a point. Men tend to run businesses in Bangladesh, and their survey shows how the female clients simply pass on the microcredit to their male counterparts. The respondents note the following as reasons for dependence on men (p.g. 45):

inability and lack of skill of the women borrowers,

more investment opportunities in man-relevant activities,

male-dominated family structure where male members maintain and control family,

social environment and custom where business activities are considered to be men’s work, and

women are not expected or respected in the domain of men’s activities (business activities)

So .. Why don’t MFIs simply lend to men? Blind ideology, or is this something based on reality?

Google “men microfinance” and you’ll get a ton of useful discussion, interspersed by a couple of good studies on this issue. The short answer is that we may not always know why, but men tend make for crappy borrowers. There is something in the woman-borrower/man-entrepreneur dynamic that “works.” (But may not always “work” in a way that is comforting – check out Lamia Karim’s work for societal dynamics gone bad.) Man-borrower-entrepreneur models don’t tend to work.

It is not constructive to simply take out the borrower intermediary when she clearly has something big to do with things.

It is also why SME lending has been so hard.

There are bunch of other things that gave me reason for pause, including:

The study relies too much on the Grameen model. BRAC and specially ASA do not do things like Grameen, and the results might be quite different for them. The authors may find that the “stereotyped microcredit delivery system” may have considerable variation within it.

There is no “counterfactual” to the claim that “microcredit is not sufficiently productive to generate enough revenue for interest payments if market rate wages are paid for family labour” for a significant portion of the borrowers. What if they did not borrow? Would they earn more? Would they starve?

It calls 25%-65% interest rates exorbitant, citing Bangladesh Bank lending rates of 4-5%, and commercial lending rates of 10-12% (p.g. 42). 65% could be considered exorbitant, but 25%? And most importantly, there are very, very real reasons why microfinance interest rates are so high. And it’s not because Grameen/BRAC/ASA are wannabe loan-sharks.

Its citations are .. unimpressive. One study used to comment on male-female gender dynamics is from 1996 – arguably an eternity in terms of the evolution of microfinance (p.g. 45). Commercial lending rates are quoted from 1997 (p.g. 42). More than half the references are the authors’ own, and the rest are mostly links to MFI reports.

Overall, this piece has decent analysis behind it. I think it gets into trouble trying to hammer out conclusions from it that are not adequately supported by the data.

By the way, if 11.7% of the (non-random) sample are microentrepreneurs of some stripe, what about the remaining 88.3%? What are they using microcredit for? If microcredit has limitations for “promoting microenterprises in Bangladesh,” what is it overwhelmingly succeeding in doing?